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SageMaker Feature Store adds Lake Formation, Iceberg support

By PulseAugur Editorial · [2 sources] ·

Amazon SageMaker Feature Store has introduced new capabilities to enhance ML feature pipelines. These updates include native integration with AWS Lake Formation for fine-grained access control and new Apache Iceberg table properties to manage metadata accumulation and reduce storage costs. The enhancements are available through SageMaker Python SDK v3.8.0, aiming to streamline feature data management and cost predictability for machine learning operations. AI

IMPACT Improves efficiency and cost management for ML feature pipelines, potentially accelerating production deployments.

RANK_REASON Product update for an existing ML platform feature.

Read on AWS Machine Learning Blog →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

SageMaker Feature Store adds Lake Formation, Iceberg support

COVERAGE [2]

  1. AWS Machine Learning Blog TIER_1 English(EN) · Dhaval Shah ·

    Accelerate ML feature pipelines with new capabilities in Amazon SageMaker Feature Store

    Today, we’re announcing three new capabilities available in SageMaker Python SDK v3.8.0. In this post, we walk through each capability with code examples you can use to get started. For complete end-to-end walkthroughs, see the accompanying notebooks for Lake Formation governance…

  2. Mastodon — sigmoid.social TIER_1 English(EN) · [email protected] ·

    🤖 Accelerate ML feature pipelines with new capabilities in Amazon SageMaker Feature Store Today, we’re announcing three new capabilities available in SageMaker

    🤖 Accelerate ML feature pipelines with new capabilities in Amazon SageMaker Feature Store Today, we’re announcing three new capabilities available in SageMaker Python SDK v3.8.0. In this post, we walk through each capability with code examples you can use to get started. For comp…

    LINKS aws.amazon.com/…/accelerate-ml-feature-pi…

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